dpnp.allclose

dpnp.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[source]

Returns True if two arrays are element-wise equal within a tolerance.

The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.

NaNs are treated as equal if they are in the same place and if equal_nan=True. Infs are treated as equal if they are in the same place and of the same sign in both arrays.

For full documentation refer to numpy.allclose.

Parameters:

a ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have numeric data type. Both inputs a and b can not be scalars at the same time.
b ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have numeric data type. Both inputs a and b can not be scalars at the same time.
rtol ({dpnp.ndarray, usm_ndarray, scalar}, optional) -- The relative tolerance parameter. Default: 1e-05.
atol ({dpnp.ndarray, usm_ndarray, scalar}, optional) -- The absolute tolerance parameter. Default: 1e-08.
equal_nan (bool) -- Whether to compare NaNs as equal. If True, NaNs in a will be considered equal to NaNs in b in the output array. Default: False.

Returns:

out -- A 0-dim array with True value if the two arrays are equal within the given tolerance; with False otherwise.

Return type:

dpnp.ndarray

See also

dpnp.isclose: Test whether two arrays are element-wise equal.
dpnp.all: Test whether all elements evaluate to True.
dpnp.any: Test whether any element evaluates to True.
dpnp.equal: Return (x1 == x2) element-wise.

Notes

The comparison of a and b uses standard broadcasting, which means that a and b need not have the same shape in order for dpnp.allclose(a, b) to evaluate to True. The same is true for dpnp.equal but not dpnp.array_equal.

Examples

>>> import dpnp as np
>>> a = np.array([1e10, 1e-7])
>>> b = np.array([1.00001e10, 1e-8])
>>> np.allclose(a, b)
array(False)

>>> a = np.array([1.0, np.nan])
>>> b = np.array([1.0, np.nan])
>>> np.allclose(a, b)
array(False)
>>> np.allclose(a, b, equal_nan=True)
array(True)

>>> a = np.array([1.0, np.inf])
>>> b = np.array([1.0, np.inf])
>>> np.allclose(a, b)
array(True)